Events

Upcoming Events

Mon
Oct 08
3:00 PM
EER 3.646 - Blaschke Conference Room

Recent years have witnessed significant progress in entropy estimation, in particular in the large alphabet regime. Concretely, there exist efficiently computable information theoretically optimal estimators whose performance with n samples is essentially that of the maximum likelihood estimator with n log(n) samples, a phenomenon termed ``effective sample size boosting''. Generalizations to processes with memory (estimation of the entropy rate) and continuous distributions (estimation of the differential entropy) have remained largely open.

Tue
Oct 09
11:00 AM
EER 3.646 - Blaschke Conference Room

Soft biomaterials such as human skin have very different mechanical properties from conventional electronics, requiring unusual materials and geometries to match the behavior of the skin.  One of the biggest challenges in stretchable electronics is the transfer of power and data signals, with physical wiring easily pulled out or damaged.  In my talk, I will be discussing all aspects of creating inductors and power circuits for wireless power transfer to stretchable systems.  I will focus on the use of room temperature liquid metals and stretchable magnetic materials to maximize power trans

Recent Events

31 Aug 2018
Bio-tissues are soft, curvilinear and dynamic whereas wafer-based electronics are hard, planar, and rigid. Over the past decade, stretchable high-performance inorganic electronics have emerged as a result of new structural designs and unique materials processes. Electronic tattoos (e-tattoos) represent a class of stretchable circuits, sensors, and stimulators that are ultrathin, ultrasoft and skin-conformable. This talk will first introduce stretchable serpentine structures followed by a dry and freeform “cut-and-paste” method for the rapid prototyping of e-tattoos.
01 Mar 2018
Inspired by the recent advances in deep learning, we propose a novel iterative BP-CNN architecture for channel decoding under correlated noise. This architecture concatenates a trained convolutional neural network (CNN) with a standard belief-propagation (BP) decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and hence result in better decoding performance.
15 Feb 2018

This talk will describe several approaches to reducing energy consumption in internet-of-things applications and applications of data analytics to neuro-psychiatric disorders. Machine learning and information analytics are important components in all these things. Almost all things should have embedded classifiers to make decisions on data. Thus, reducing energy consumption of features and classifiers is important. First part of the talk will present energy reduction approaches from feature selection, classification and incremental multi-stage classification perspectives.

10 Nov 2017

We are witnessing an unprecedented growth in the amount of data that is being collected and made available for data mining. While the availability of large-scale datasets presents exciting opportunities for advancing sciences, healthcare, understanding of human behavior etc., mining the data set for useful information becomes a computationally challenging task. We are in an era where the volume of data is growing faster than the rate at which available computing power is growing, thereby creating a dire need for computationally efficient algorithms for data mining.